Centralities of Nodes and Influences of Layers in Large Multiplex Networks

Abstract

We formulate and propose an algorithm (MultiRank) for the ranking of nodes and layers in large multiplex networks. MultiRank takes into account the full multiplex network structure of the data and exploits the dual nature of the network in terms of nodes and layers. The proposed centrality of the layers (influences) and the centrality of the nodes are determined by a coupled set of equations. The basic idea consists in assigning more centrality to nodes that receive links from highly influential layers and from already central nodes. The layers are more influential if highly central nodes are active in them. The algorithm applies to directed/undirected as well as to weighted/unweighted multiplex networks. We discuss the application of MultiRank to three major examples of multiplex network datasets: the European Air Transportation Multiplex Network, the Pierre Auger Multiplex Collaboration Network and the FAO Multiplex Trade Network.

(12 pages, 7 figures)

References in corpus (13)

• The structure and dynamics of multilayer networks
• The Product Space Conditions the Development of Nations
• Diffusion dynamics on multiplex networks
• Multirelational Organization of Large-scale Social Networks in an Online World
• Layer aggregation and reducibility of multilayer interconnected networks
• Emergence of network features from multiplexity
• MuxViz: A Tool for Multilayer Analysis and Visualization of Networks
• Diffusion of scientific credits and the ranking of scientists
• The multilayer connectome of Caenorhabditis elegans
• Redundant interdependencies boost the robustness of multilayer networks
• Functional Multiplex PageRank
• Ranking species in mutualistic networks
• Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach

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• Node-layer duality in networked systems
• LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks